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Yogesh Bawa
Yogesh Bawa

We are in a tech-driven world fueled by digital transformation and innovation. Most businesses also know they must adapt quickly to keep pace with this rapid technological evolution. What does all this mean for product development? It means that the ever-evolving expectations of consumers demand a corresponding innovation in how we develop and test products. It requires innovative test solutions and automation with a high order of sophistication and efficiency in testing. This is where Artificial Intelligence (AI) can make a difference.

Prod Dev 2.0: How AI is changing ‘product development’ with performance testing

Performance testing is one sure way to ensure a seamless user experience for any product. But it still comprises some areas which are heavily dependent on manual efforts. Whenever there’s less time for performance testing or due to the fast-paced nature of some projects, some anomalies or errors will be injected.

AI has already proven its prowess in areas like functional automation, failure prediction, QA process improvements, etc. It’s now time to explore the AI avenues in performance testing.

  • 23% of executives believe that AI & ML functionalities would benefit performance test automation.

  • Ever since the pandemic, there has been a significant increase in online businesses.38% of executives believe they need to focus more on performance validations.

  • 24% of executives believe that QA must develop data analytics and AI skills.


Data source: World Quality Report 2021 & 2021-2022.

Performance testing areas where AI can help

Any end-to-end performance test lifecycle consists of many challenges, from test planning to test execution to finding out the performance bottleneck, locating the root cause, and finally, fixing the issue.

By implementing AI in performance testing, many of these challenges can either be resolved fully or at least make life easier for any performance test engineer.

Here are some of the challenges which can be easily resolved by amalgamating AI in the Performance testing lifecycle -

  • When non-functional requirements are either not defined properly or, in some cases, don’t even exist.
  • Much time is spent simulating the real user journey into a script. This is often due to complex technology.
  • During the execution, performance testers get loads and loads of data to analyze. And as you would know, data analysis – without any intelligent systems – is a complex task.
  • When despite thorough analysis, identifying a performance bottleneck in complex systems is an extremely time-consuming and challenging task.

These are only some of the challenges AI can greatly help.

Rewiring performance engineering challenges using AI concepts

1. Using AI to develop performance test scripts

With the help of AI and NLP, we can make code-less automated scripts for Performance Testing. It can help performance engineers to develop scripts with barely any coding efforts. AI-based scripting tools use auto-correlation and auto-parameterization to handle an application’s dynamic content.

This can greatly help develop performance scripts for tech-based applications like Salesforce, SAP applications, Siebel CRM, JD Edwards, and Single Sign on.

For applications that have been built on these technologies, correlation is not only a very time-consuming process but is also quite challenging.

AI scripting can help reduce efforts by as much as 40%.

2. Workload Modelling

We can use AI-based engines to predict the future usage patterns of the applications. These tools can help us analyze production trends and perform better and more realistic workload modeling.

For example, Google Analytics, based on AI, provides different usage patterns. These patterns can be very useful in defining a real-time load model that comes closest to simulating a real one.

3. Result Analysis

For any performance engineer, result analysis is a very crucial step, which needs a lot of expertise and experience, with a wide range in the technology stack. AI engines can help the performance engineer conduct multi-dimensional transaction error analysis and thus helps him/her find anomalies in the performance test results.

As of now, no such tool focuses on this aspect, but a tool of the framework can be a boon for any performance engineer.

4. Monitoring

During the application performance monitoring, an AI-based performance tool can generate a dependency map to get real-time performance analysis and help with root cause analysis.

For example, tools like Instana generate a Dependency Map during real-time performance monitoring.

AI can also help to predict faults in advance and generate alarms to stakeholders – all before the defect has even occurred!

Today, with much traction on resilience engineering, we hope to see such tools launch in the market soon.

5. Optimization

During optimization, rule-based code analyzer tools (developed on AI concepts) can predict code issues and can help developers write better and more optimized code snippets. For example, a tool like Deep Code helps to analyze their code while still writing it.


Today, AI has evolved and is gaining popularity within the open-source community. We need open-source AI-based engines which can be integrated with open-source performance testing tools and can give the benefits of this collaboration in real terms.

We need to encourage development in this direction. Performance engineers should take the lead and deep dive into AI. They should start developing small AI logics, which can be integrated with open-source tools like JMeter or Locust to improve the performance testing community😊.